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Laser scanning is increasingly used in many three‐dimensional (3‐D) measurement and modeling applications. It is the latest technique used in 3‐D measurement, and is becoming increasingly important within a number of applications. However, many applications require photogrammetric data in addition to laser scanning data. The purpose of this paper is to present a range and image sensor combination for three‐dimensional reconstruction of objects or scenes.

In this study, a Nikon D80 camera was mounted on an Ilris 3D laser scanner and CPP was estimated according to the laser scanner coordinate system. The estimated CPP was controlled using three different methods which were developed in this study and a sample application as coloring of point cloud using image taken by the camera mounted on the laser scanner was performed.

It was found that when a high‐resolution camera is mounted on laser scanners, camera position parameters (CPP) should be estimated very accurately with respect to the laser scanner coordinate system.

The paper shows that the combination of high‐resolution camera and laser scanners should be used for more accurate and efficient results in 3D modeling applications.

This study aims to develop an optimized 3D laser point reconstruction using Descent Gradient algorithm. Precise and accurate reconstruction of 3D laser point cloud of the complex environment/object is a key solution for many industries such as construction, gaming, automobiles, aerial navigation, architecture and automation. A 2D laser scanner along with a servo motor/pan tilt/inertial measurement unit is used for generating 3D point cloud (either environment/object or both) by acquiring the real-time data from sensors. However, while generating the 3D laser point cloud, various problems related to time synchronization problem between laser and servomotor and torque variation in servomotors arise, which causes misalignment in stacking the 2D laser scan for generating the 3D point cloud of the environment. Because of the misalignment in stacking, the 2D laser scan corresponding to the erroneous angular and position information by the servomotor and the 3D laser point cloud become distorted in terms of inconsistency for measuring the dimension of the objects.

This paper addresses a modified 3D laser system assembled from a 2D laser scanner coupled with a servomotor (dynamixel motor) for developing an efficient 3D laser point cloud with the implementation of an optimization technique: descent gradient filter (DGT). The proposed approach reduces the cost function (error) in the angular and position coordinates of the servo motor caused because of torque variation and time synchronization, which resulted in enhancing the accuracy in 3D point cloud mapping for the accurate measurement of the object’s dimensions.

Various real-world experiments are performed with the proposed DGT filter linked with laser scanner and servomotor and an improvement of 6.5 per cent in measuring the accurate dimension of object is obtained while comparing with conventional approaches for generating a 3D laser point cloud.

This proposed technique may be applicable for various industrial applications that are based on robotics arms (such as painting, welding and cutting) in the automobile industry, the optimized measurement of object, efficient mobile robot navigation, precise 3D reconstruction of environment/object in construction, architecture applications, airborne applications and aerial navigation.

Laser scanning technique is used to measure and model objects using point cloud data generated laser pulses. Conventional techniques to construct 3D models are time consuming, costly and need more manpower. The purpose of this paper is to assess the 3D model of the Sultan Salahuddin Abdul Aziz Shah Mosque’s main dome using a terrestrial laser scanner.

A laser scanner works through line of sight, which indicates that multiple scans need to be taken from a different view to ensure a complete data set. Targets must spread in all directions, and targets should be placed on fixed structures and flat surfaces for the normal scan and fine scan. After the scanning operation, point cloud data from the laser scanner were cleaned and registered before a 3D model could be developed.

As a result, the reconstruction of the 3D model was successfully developed. The samples are based on the triangle dimension, curve line, horizontal dimension and vertical dimension at the dome. The standard deviation and accuracy are calculated based on the comparison of the 21 samples taken between the high-resolution and low-resolution scanning data.

There are many ways to develop the 3D model and based on this study, the less complex ways also produce the best result. The authors implement the different types of dimensions for the 3D model assessment, which have not yet been considered in the past.

The purpose of this paper is to introduce the development of an innovative mobile laser scanning (MLS) method for 3D indoor mapping. The generally accepted and used procedure for this type of mapping is usually performed using static terrestrial laser scanning (TLS) which is high-cost and time-consuming. Compared with conventional TLS, the developed method proposes a new idea with advantages of low-cost, high mobility and time saving on the implementation of a 3D indoor mapping.

This method integrates a low-cost 2D laser scanner with two indoor positioning techniques – ultra-wide band (UWB) and an inertial measurement unit (IMU), to implement a 3D MLS for reality captures from an experimental indoor environment through developed programming algorithms. In addition, a reference experiment by using conventional TLS was also conducted under the same conditions for scan result comparison to validate the feasibility of the developed method.

The findings include: preset UWB system integrated with a low-cost IMU can provide a reliable positioning method for indoor environment; scan results from a portable 2D laser scanner integrated with a motion trajectory from the IMU/UWB positioning approach is able to generate a 3D point cloud based in an indoor environment; and the limitations on hardware, accuracy, automation and the positioning approach are also summarized in this study.

As the main advantage of the developed method is low-cost, it may limit the automation of the method due to the consideration of the cost control. Robotic carriers and higher-performance 2D laser scanners can be applied to realize panoramic and higher-quality scan results for improvements of the method.

Moreover, during the practical application, the UWB system can be disturbed by variances of the indoor environment, which can affect the positioning accuracy in practice. More advanced algorithms are also needed to optimize the automatic data processing for reducing errors caused by manual operations.

The development of this MLS method provides a novel idea that integrates data from heterogeneous systems or sensors to realize a practical aim of indoor mapping, and meanwhile promote the current laser scanning technology to a lower-cost, more flexible, more portable and less time-consuming trend.

The purpose of this study is to explore the suggestions that construction processes could be considerably improved by integrating building information modelling (BIM) with 3D laser scanning technologies. This case study integrated 3D laser point cloud scans with BIM to explore the effects of BIM adoption on ongoing construction project, whilst evaluating the utility of 3D laser scanning technology for producing structural 3D models by converting point cloud data (PCD) into BIM.

The primary data acquisition adopted the use of Trimble X7 laser scanning process, which is a set of data points in the scanned space that represent the scanned structure. The implementation of BIM with the 3D PCD to explore the precision and effectiveness of the construction processes as well as the as-built condition of a structure was precisely captured using the 3D laser scanning technology to recreate accurate and exact 3D models capable of being used to find and fix problems during construction.

The findings indicate that the integration of BIM and 3D laser scanning technology has the tendency to mitigate issues such as building rework, improved project completion times, reduced project cost, enhanced interdisciplinary communication, cooperation and collaboration amongst the project duty holders, which ultimately enhances the overall efficiency of the construction project.

The acquisition of data using 3D laser scanner is usually conducted from the ground. Therefore, certain aspects of the building could potentially disturb data acquisition; for example, the gable and sections of eaves (fascia and soffit) could be left in a blind spot. Data acquisition using 3D laser scanner technology takes time, and the processing of the vast amount of data acquired is laborious, and if not carefully analysed, could result in errors in generated models. Furthermore, because this was an ongoing construction project, material stockpiling and planned construction works obstructed and delayed the seamless capture of scanned data points.

These findings highlight the significance of integrating BIM and 3D laser scanning technology in the construction process and emphasise the value of advanced data collection methods for effectively managing construction projects and streamlined workflows.

Facility management gained profound importance due to the increasing complexity of different systems and the cost of operation and maintenance. However, due to the increasing complexity of different systems, facility managers may suffer from a lack of information. The purpose of this paper is to propose a new facility management approach that links segmented assets to the vital data required for managing facilities.

Automatic point cloud segmentation is one of the most crucial processes required for modelling building facilities. In this research, laser scanning is used for point cloud acquisition. The research utilises region growing algorithm, colour-based region-growing algorithm and Euclidean cluster algorithm.

A case study is worked out to test the accuracy of the considered point cloud segmentation algorithms utilising metrics precision, recall and F-score. The results indicate that Euclidean cluster extraction and region growing algorithm revealed high accuracy for segmentation.

The research presents a comparative approach for selecting the most appropriate segmentation approach required for accurate modelling. As such, the segmented assets can be linked easily with the data required for facility management.

The purpose of this research is to outline in detail the procedure of remote data capture using laser scanning and the subsequent processing required in order to identify a new methodology for creating full engineering drawings (orthographic and 3D models) from laser scan and image survey data for historic structures.

Historic building information modelling (HBIM) is proposed as a new system of modelling historic structures; the HBIM process begins with remote collection of survey data using a terrestrial laser scanner combined with digital cameras. A range of software programs is then used to combine the image and scan data.

Meshing of the point cloud followed by texturing from the image data creates a framework for the creation of a 3D model. Mapping of BIM objects onto the 3D surface model is the final stage in the reverse engineering process, creating full 2D and 3D models including detail behind the object's surface concerning its methods of construction and material makeup, this new process is described as HBIM.

The future research within this area will concentrate on three main stands. The initial strand is to attempt improve the application of geometric descriptive language to build complex parametric objects. The second stand is the development of a library of parametric based on historic data (from Vitruvius to 18th century architectural pattern books). Finally, while it is possible to plot parametric objects onto the laser scan data, there is need to identify intermediate software platforms to accelerate this stage within the HBIM framework.

The ability to customise garments for fit is directly tied to the availability of a comprehensive, accurate set of measurements. To obtain accurate physical measurements, a basic knowledge and set of skills are required that are not often found in the average salesperson at a retail clothing outlet. The development of three‐dimensional body‐scanning technologies may have significant potential for use in the apparel industry, particularly for customisation or mass customisation strategies to be employed. The purpose of this study was to review all the 3D body scanning systems currently available and to determine the underlying principles that allow these systems to work. Specifications of each system were compared in order to provide some direction for further research into the integration of these systems with current apparel CAD pattern design or pattern generation technologies.

The purpose of this paper is to detect traversable regions surrounding a mobile robot by computing terrain unevenness using the range data obtained from a single 3D scan.

The geometry of acquiring range data from a 3D scan is exploited to probe the terrain and extract traversable regions. Nature of terrain under each scan point is quantified in terms of an unevenness value, which is computed from the difference in range of scan point with respect to its neighbours. Both radial and transverse unevenness values are computed and compared with threshold values at every point to determine if the point belongs to a traversable region or an obstacle. A region growing algorithm spreads like a wavefront to join all traversable points into a traversable region.

This simple method clearly distinguishes ground and obstacle points. The method works well even in presence of terrain slopes or when the robot experiences pitch and roll.

The method applies on single 3D scans and not on aggregated point cloud in general.

The method has been tested on a mobile robot in outdoor environment in our research centre.

This method, along with advanced navigation schemes, can reduce human intervention in many mobile robot applications including unmanned ground vehicles.

Range difference between scan points has been used earlier for obstacle detection, but no methodology has been developed around this concept. The authors propose a concrete method based on computation of radial and transverse unevenness at every point and detecting obstacle edges using range-dependent threshold values.

This paper aims to propose a series of approaches to solve the problem of the mobile robot motion control and autonomous navigation in large-scale outdoor GPS-denied environments.

Based on the model of mobile robot with two driving wheels, a controller is designed and tested in obstacle-cluttered scenes in this paper. By using the priori “topology-geometry” map constructed based on the odometer data and the online matching algorithm of 3D-laser scanning points, a novel approach of outdoor localization with 3D-laser scanner is proposed to solve the problem of poor localization accuracy in GPS-denied environments. A path planning strategy based on geometric feature analysis and priority evaluation algorithm is also adopted to ensure the safety and reliability of mobile robot’s autonomous navigation and control.

A series of experiments are conducted with a self-designed mobile robot platform in large-scale outdoor environments, and the experimental results show the validity and effectiveness of the proposed approach.

The problem of motion control for a differential drive mobile robot is investigated in this paper first. At the same time, a novel approach of outdoor localization with 3D-laser scanner is proposed to solve the problem of poor localization accuracy in GPS-denied environments. A path planning strategy based on geometric feature analysis and priority evaluation algorithm is also adopted to ensure the safety and reliability of mobile robot’s autonomous navigation and control.